IDEAS home Printed from https://ideas.repec.org/a/sae/jothpo/v24y2012i1p46-65.html
   My bibliography  Save this article

A model of political information-processing and learning cooperation in the repeated Prisoner’s Dilemma

Author

Listed:
  • Sung-youn Kim

Abstract

A model of political information processing drawn from the studies of political behavior and psychology is applied to the emergence of cooperation observed in classic repeated Prisoner’s Dilemma (PD) game experiments. The results show that the model can robustly account for the learning of cooperation observed in the experiments when players are aware of the strategic nature of the game and make choices over immediate actions. In effect, basic psychological learning mechanisms, well-established in political behavior and psychology research, together tend to lead players to learn to cooperate over time under quite general conditions. In particular, the evaluative affect players develop towards choice objects and a belief learning that weighs an actually obtained outcome more than a forgone outcome play a central role in these processes.

Suggested Citation

  • Sung-youn Kim, 2012. "A model of political information-processing and learning cooperation in the repeated Prisoner’s Dilemma," Journal of Theoretical Politics, , vol. 24(1), pages 46-65, January.
    • Handle: RePEc:sae:jothpo:v:24:y:2012:i:1:p:46-65
    DOI: 10.1177/0951629811411748
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0951629811411748
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0951629811411748?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kreps, David M. & Milgrom, Paul & Roberts, John & Wilson, Robert, 1982. "Rational cooperation in the finitely repeated prisoners' dilemma," Journal of Economic Theory, Elsevier, vol. 27(2), pages 245-252, August.
    2. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    3. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
    4. Ernst Fehr & Klaus M. Schmidt, 1999. "A Theory of Fairness, Competition, and Cooperation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(3), pages 817-868.
    5. Lodge, Milton & Steenbergen, Marco R. & Brau, Shawn, 1995. "The Responsive Voter: Campaign Information and the Dynamics of Candidate Evaluation," American Political Science Review, Cambridge University Press, vol. 89(2), pages 309-326, June.
    6. Jonathan Bendor & Dilip Mookherjee & Debraj Ray, 2001. "Aspiration-Based Reinforcement Learning In Repeated Interaction Games: An Overview," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 3(02n03), pages 159-174.
    7. Yoella Bereby-Meyer & Alvin E. Roth, 2006. "The Speed of Learning in Noisy Games: Partial Reinforcement and the Sustainability of Cooperation," American Economic Review, American Economic Association, vol. 96(4), pages 1029-1042, September.
    8. Rabin, Matthew, 1993. "Incorporating Fairness into Game Theory and Economics," American Economic Review, American Economic Association, vol. 83(5), pages 1281-1302, December.
    9. Martin A. Nowak & Akira Sasaki & Christine Taylor & Drew Fudenberg, 2004. "Emergence of cooperation and evolutionary stability in finite populations," Nature, Nature, vol. 428(6983), pages 646-650, April.
    10. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    11. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, April.
    12. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Gotts, Nicholas M. & Polhill, J. Gary, 2007. "Transient and asymptotic dynamics of reinforcement learning in games," Games and Economic Behavior, Elsevier, vol. 61(2), pages 259-276, November.
    13. Bendor Jonathan & Mookherjee Dilip & Ray Debraj, 2001. "Reinforcement Learning in Repeated Interaction Games," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 1(1), pages 1-44, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nick Feltovich, 2000. "Reinforcement-Based vs. Belief-Based Learning Models in Experimental Asymmetric-Information," Econometrica, Econometric Society, vol. 68(3), pages 605-642, May.
    2. James Andreoni & Marco Castillo & Ragan Petrie, 2009. "Revealing Preferences for Fairness in Ultimatum Bargaining," Korean Economic Review, Korean Economic Association, vol. 25, pages 35-63.
    3. Andreozzi, Luciano, 2013. "Learning to be fair," Journal of Economic Behavior & Organization, Elsevier, vol. 90(C), pages 181-195.
    4. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    5. Jacob K. Goeree & Charles A. Holt, 2001. "Ten Little Treasures of Game Theory and Ten Intuitive Contradictions," American Economic Review, American Economic Association, vol. 91(5), pages 1402-1422, December.
    6. Belianin, A., 2017. "Face to Face to Human Being: Achievements and Challenges of Behavioral Economics," Journal of the New Economic Association, New Economic Association, vol. 34(2), pages 166-175.
    7. E. J. Anderson & T. D. H. Cau, 2009. "Modeling Implicit Collusion Using Coevolution," Operations Research, INFORMS, vol. 57(2), pages 439-455, April.
    8. Haruvy, Ernan & Stahl, Dale O., 2012. "Between-game rule learning in dissimilar symmetric normal-form games," Games and Economic Behavior, Elsevier, vol. 74(1), pages 208-221.
    9. Shafran, Aric P., 2012. "Learning in games with risky payoffs," Games and Economic Behavior, Elsevier, vol. 75(1), pages 354-371.
    10. V. P. Crawford, 2014. "Boundedly rational versus optimization-based models of strategic thinking and learning in games," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 5.
    11. Ido Erev & Alvin Roth & Robert Slonim & Greg Barron, 2007. "Learning and equilibrium as useful approximations: Accuracy of prediction on randomly selected constant sum games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 33(1), pages 29-51, October.
    12. Ianni, Antonella, 2014. "Learning strict Nash equilibria through reinforcement," Journal of Mathematical Economics, Elsevier, vol. 50(C), pages 148-155.
    13. Oyarzun, Carlos & Sarin, Rajiv, 2013. "Learning and risk aversion," Journal of Economic Theory, Elsevier, vol. 148(1), pages 196-225.
    14. Abbink, Klaus & Bolton, Gary E. & Sadrieh, Abdolkarim & Tang, Fang-Fang, 2001. "Adaptive Learning versus Punishment in Ultimatum Bargaining," Games and Economic Behavior, Elsevier, vol. 37(1), pages 1-25, October.
    15. Lacomba, Juan A. & Lagos, Francisco & Reuben, Ernesto & van Winden, Frans, 2014. "On the escalation and de-escalation of conflict," Games and Economic Behavior, Elsevier, vol. 86(C), pages 40-57.
    16. Ianni, A., 2002. "Reinforcement learning and the power law of practice: some analytical results," Discussion Paper Series In Economics And Econometrics 203, Economics Division, School of Social Sciences, University of Southampton.
    17. Siegfried Berninghaus & Werner Güth & M. Vittoria Levati & Jianying Qiu, 2006. "Satisficing in sales competition: experimental evidence," Papers on Strategic Interaction 2006-32, Max Planck Institute of Economics, Strategic Interaction Group.
    18. Anthony Ziegelmeyer & Frédéric Koessler & Kene Boun My & Laurent Denant-Boèmont, 2008. "Road Traffic Congestion and Public Information: An Experimental Investigation," Journal of Transport Economics and Policy, University of Bath, vol. 42(1), pages 43-82, January.
    19. DeJong, D.V. & Blume, A. & Neumann, G., 1998. "Learning in Sender-Receiver Games," Other publications TiSEM 4a8b4f46-f30b-4ad2-bb0c-1, Tilburg University, School of Economics and Management.
    20. Jean-François Laslier & Bernard Walliser, 2015. "Stubborn learning," Theory and Decision, Springer, vol. 79(1), pages 51-93, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:jothpo:v:24:y:2012:i:1:p:46-65. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.